{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
       "0                 0.07871  ...         25.38          17.33           184.60   \n",
       "1                 0.05667  ...         24.99          23.41           158.80   \n",
       "2                 0.05999  ...         23.57          25.53           152.50   \n",
       "3                 0.09744  ...         14.91          26.50            98.87   \n",
       "4                 0.05883  ...         22.54          16.67           152.20   \n",
       "\n",
       "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   worst concave points  worst symmetry  worst fractal dimension  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from BorutaShap import BorutaShap, load_data\n",
    "\n",
    "X,y = load_data()\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [01:04<00:00,  1.54it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13 attributes confirmed important: ['worst radius', 'mean area', 'worst smoothness', 'area error', 'worst texture', 'worst concavity', 'mean texture', 'worst area', 'mean concave points', 'worst concave points', 'radius error', 'worst perimeter', 'mean concavity']\n",
      "16 attributes confirmed unimportant: ['mean symmetry', 'worst fractal dimension', 'concavity error', 'mean perimeter', 'mean fractal dimension', 'concave points error', 'symmetry error', 'worst compactness', 'worst symmetry', 'fractal dimension error', 'mean compactness', 'mean radius', 'smoothness error', 'compactness error', 'mean smoothness', 'texture error']\n",
      "1 tentative attributes remains: ['perimeter error']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# no model selected default is Random Forest, if classification is True it is a Classification problem\n",
    "Feature_Selector = BorutaShap(importance_measure='shap',\n",
    "                              classification=False)\n",
    "\n",
    "Feature_Selector.fit(X=X, y=y, n_trials=100, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Returns Boxplot of features\n",
    "Feature_Selector.plot(which_features='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the results to csv filename is called feature_importance\n",
    "Feature_Selector.results_to_csv(filename='feature_importance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.6 64-bit ('borutatest': conda)",
   "language": "python",
   "name": "python37664bitborutatestcondaeccbb2ba2d924cbf80a10c62689910e2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
